Variation in heart rate influences the assessment of transient ischemic dilation in myocardial perfusion scintigraphy
Bibliographic record
Abstract
BACKGROUND: Transient arrhythmias can affect transient ischemic dilation (TID) ratios. This study was initiated to evaluate the frequency and effect of normal heart rate change on TID measures in routine clinical practice. METHODS: Consecutive patients undergoing stress/rest sestamibi gated myocardial perfusion scintigraphy were studied (N = 407). Heart rate at the time of stress and rest imaging were recorded. TID ratios were analyzed in relation to absolute change in heart rate (stress minus rest) for subjects with normal perfusion and systolic function (Group 1, N = 169) and those with abnormalities in perfusion and/or function (Group 2, N = 238). RESULTS: In Group 1, mean TID ratio was inversely correlated with the change in heart rate (r = -0.47, P < 0.0001). For every increase of 10 BPM in heart rate change, the TID ratio decreased by approximately 0.06 (95% confidence interval 0.04-0.07). In Group 2, multiple linear regression demonstrated that the change in heart rate (beta = -0.25, P < 0.0001) and the summed difference score (beta = 0.36, P < 0.0001) were independent predictors of the TID ratio. CONCLUSION: Normal variation in heart rate between the stress and rest components of myocardial perfusion scans is common and can influence TID ratios in patients with normal and abnormal cardiac scans.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".